Deep supervised learning using local errors

Understand

Error backpropagation is a highly effective mechanism for learning high-quality hierarchical features in deep networks.

• Updating the features or weights in one layer, however, requires waiting for the propagation of error signals from higher layers.
• Learning using delayed and non-local errors makes it hard to reconcile backpropagation with the learning mechanisms observed in biological neural networks as it requires the neurons to maintain a memory of the input long enough until the higher-layer errors arrive.
• In this paper, we propose an alternative learning mechanism where errors are generated locally in each layer using fixed, random auxiliary classifiers.